Object tracking is an important research domain in computer vision. Object tracking is the basis for detailed analysis of an object. Based on the object tracking, object trajectory and behavioral analysis can be implemented. Currently, there are two types of object tracking models in the academic field: a recognition-based tracker and a generation-based tracker.
In general, a recognition-based tracker is better than a generation-based tracker. Online machine learning is generally required for the recognition-based tracker. Further, a classifier generated through online machine learning is used to identify objects.
In general, recognition-based tracking algorithms can adapt to object change in a certain extent and are robust. But the recognition-based tracking algorithms require a large number of training samples and the training process is very time-consuming. It is difficult for the recognition-based tracking algorithms to solve multi-scale problems. Therefore, to overcome disadvantages of the recognition-based tracking algorithms, a circulant matrix method can be used to obtain the training samples. On one hand, sufficient number of training samples can be obtained to train a classifier with a higher recognition rate; on the other hand, according to characteristics of the circulant matrix, Fourier transform and kernel trick are used to reduce the time required for training the classifier. Thus, the method can resolve the problem of training sample and reduce training time. However, the method cannot solve the multi-scale problems and cannot accelerate Fourier transform. In addition, the method cannot be extended to multi-object tracking scenarios.
The disclosed methods and systems are directed to solve one or more problems set forth above and other problems. For example, the high-speed automatic multi-object tracking method with kernelized correlation filters can be applied in battlefield surveillance, video monitoring, image compression, image retrieve, human-computer interaction, and so on.